These algorithms are becoming popular after many years in the wilderness. The name comes from the realization that the addition of increasing numbers of layers typically in a neural network enables a model to learn increasingly complex representations of the data.

There are numerous techniques for creating algorithms that are capable of learning and adapting over time. Broadly speaking, we can organize these algorithms into one of three categories – supervised, unsupervised, and reinforcement learning.

Supervised learning refers to situations in which each instance of input data is accompanied by a desired or target value for that input. When the target values are a set of finite discrete categories, the learning task is often known as a classification problem. When the targets are one or more continuous variables, the task is called regression.

“The original question ‘Can machines think?’ I believe to be too meaningless to deserve discussion. Nevertheless, I believe that at the end of the century, the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.” – Alan Turing

“We may compare a man in the process of computing a real number to a machine which is only capable of a finite number of conditions…” – Alan Turing

It is difficult to tell the history of AI without first describing the formalization of computation and what it means for something to compute. The primary impetus towards formalization came down to a question posed by the mathematician David Hilbert in 1928.

The random forest (RF) model, first proposed by Tin Kam Ho in 1995, is a subclass of ensemble learning methods that is applied to classification and regression. An ensemble method constructs a set of classifiers – a group of decision trees, in the case of RF – and determines the label for each data instance by taking the weighted average of each classifier’s output.

The learning algorithm utilizes the divide-and-conquer approach and reduces the inherent variance of a single instance of the model through bootstrapping. Therefore, “ensembling” a group of weaker classifiers boosts the performance and the resulting aggregated classifier is a stronger model.

Cisco recently announced the term “intent-based networking” in a press release that pushes the idea that networks need to be more intuitive. One element of that intuition is for networks to be more secure without requiring a lot of heavy lifting by local network security professionals. And a featured part of that strategy is Cisco ETA:

"Cisco's Encrypted Traffic Analytics solves a network security challenge previously thought to be unsolvable," said David Goeckeler, senior vice president and general manager of networking and security. "ETA uses Cisco's Talos cyber intelligence to detect known attack signatures even in encrypted traffic, helping to ensure security while maintaining privacy."

Whether the task is driving a nail, fastening a screw, or detecting a hidden HTTP tunnel, it pays to have the right tool for the job. The wrong tool can increase the time to accomplish a task, waste valuable resources, or worse. Leveraging the power of machine learning is no different.

Vectra has adopted the philosophy of implementing the most optimal machine learning tool for each attacker behavior detection algorithm. Each method has its own strengths.

Sometimes science fiction becomes less fantastic over time than the actual reality. Take the film Ghost in the Shell, for example, which hits the big screen this week. It’s an adaptation of the fictional 28-year-old cult classic Japanese manga about human and machine augmentation.